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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.31

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the bigbio/quantms analysis pipeline. For information about how to interpret these results, please see the documentation.
        Report generated on 2025-09-13, 05:30 UTC based on data in: /home/runner/work/pmultiqc/pmultiqc/data

        pmultiqc

        pmultiqc is a MultiQC module to show the pipeline performance of mass spectrometry based quantification pipelines such as nf-core/quantms, MaxQuant, and DIA-NN.URL: https://github.com/bigbio/pmultiqc


        Experimental Design and Metadata

        Experimental Design

        This table shows the design of the experiment. I.e., which files and channels correspond to which sample/condition/fraction.
        You can see details about it in https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/release/latest/html/classOpenMS_1_1ExperimentalDesign.html
        Showing 2/2 rows and 7/7 columns.
        Sample NameMSstats Condition: CTMSstats Condition: CNMSstats Condition: QYMSstats BioReplicateFraction GroupFractionLabel
         
        1
        MixtureUPS10.1 fmol1
         
         ↳ RD139_Narrow_UPS1_0_1fmol_inj1
        111
         
         ↳ RD139_Narrow_UPS1_0_1fmol_inj2
        211
         
        2
        MixtureUPS10.25 fmol2
         
         ↳ RD139_Narrow_UPS1_0_25fmol_inj1
        311
         
         ↳ RD139_Narrow_UPS1_0_25fmol_inj2
        411

        Results Overview

        Summary Table

        This table shows the quantms pipeline summary statistics.
        This table shows the quantms pipeline summary statistics.
        Showing 1/1 rows.
        #Peptides Quantified#Proteins Quantified
        5682
        1540

        HeatMap

        This heatmap provides an overview of the performance of the quantms DIA (DIA-NN) results.
        This plot shows the pipeline performance overview. Some metrics are calculated. *Heatmap score[Contaminants]: as fraction of summed intensity with 0 = sample full of contaminants; 1 = no contaminants *Heatmap score[Pep Intensity (>23.0)]: Linear scale of the median intensity reaching the threshold, i.e. reaching 2^21 of 2^23 gives score 0.25. *Heatmap score[Charge]: Deviation of the charge 2 proportion from a representative Raw file (median). For typtic digests, peptides of charge 2 (one N-terminal and one at tryptic C-terminal R or K residue) should be dominant. Ionization issues (voltage?), in-source fragmentation, missed cleavages and buffer irregularities can cause a shift (see Bittremieux 2017, DOI: 10.1002/mas.21544) *Heatmap score[RT Alignment]: Compute 1 minus the mean absolute difference between 'RT' and 'Predicted.RT', and take the maximum of this value and 0. 1: |RT - Predicted.RT| = 0 *Heatmap score [ID rate over RT]: Judge column occupancy over retention time. Ideally, the LC gradient is chosen such that the number of identifications (here, after FDR filtering) is uniform over time, to ensure consistent instrument duty cycles. Sharp peaks and uneven distribution of identifications over time indicate potential for LC gradient optimization.Scored using 'Uniform' scoring function. i.e. constant receives good score, extreme shapes are bad *Heatmap score [Norm Factor]: Computes the mean absolute deviation (MAD) of 'Normalisation.Factor' from its mean. 0 = high variability in normalization factors; 1 = perfectly consistent normalization factors *Heatmap score [Peak Width]: Average peak width (RT.Stop - RT.Start). 1 = peak width equals 0; 0 = peak width equals 1 or greater
        Created with MultiQC

        Pipeline Result Statistics

        This plot shows the quantms pipeline final result.
        Including Sample Name, Possible Study Variables, identified the number of peptide in the pipeline, and identified the number of modified peptide in the pipeline, eg. All data in this table are obtained from the out_msstats file. You can also remove the decoy with the `remove_decoy` parameter.
        Showing 2/2 rows and 8/8 columns.
        Sample NameMSstats Condition: CTMSstats Condition: CNMSstats Condition: QYFraction#Peptide IDs#Unambiguous Peptide IDs#Modified Peptide IDs#Protein (group) IDs
         
        1
        MixtureUPS10.1 fmol
         
         ↳ RD139_Narrow_UPS1_0_1fmol_inj1
        1
        5295
        5295
        835
        1477
         
         ↳ RD139_Narrow_UPS1_0_1fmol_inj2
        1
        5362
        5362
        858
        1494
         
        2
        MixtureUPS10.25 fmol
         
         ↳ RD139_Narrow_UPS1_0_25fmol_inj1
        1
        5520
        5520
        881
        1510
         
         ↳ RD139_Narrow_UPS1_0_25fmol_inj2
        1
        5506
        5506
        879
        1508

        Identification Summary

        Number of Peptides identified Per Protein

        This plot shows the number of peptides per protein in quantms pipeline final result
        This statistic is extracted from the out_msstats file. Proteins supported by more peptide identifications can constitute more confident results.
        Created with MultiQC

        ProteinGroups Count

        Number of protein groups per raw file.
        Based on statistics calculated from mzTab, mzIdentML (mzid), or DIA-NN report files.
        Created with MultiQC

        Peptide ID Count

        Number of unique (i.e. not counted twice) peptide sequences including modifications per Raw file.
        Based on statistics calculated from mzTab, mzIdentML (mzid), or DIA-NN report files.
        Created with MultiQC

        Modifications Per Raw File

        Compute an occurrence table of modifications (e.g. Oxidation (M)) for all peptides, including the unmodified (but without contaminants).
        Post-translational modifications contained within the identified peptide sequence.

        The plot will show percentages, i.e. is normalized by the total number of peptide sequences (where different charge state counts as a separate peptide) per Raw file. The sum of frequencies may exceed 100% per Raw file, since a peptide can have multiple modifications.

        E.g. given three peptides in a single Raw file
        1. _M(Oxidation (M))LVLDEADEM(Oxidation (M))LNK_
        2. _(Acetyl (Protein N-term))M(Oxidation (M))YGLLLENLSEYIK_
        3. DPFIANGER

        , the following frequencies arise:

        * 33% of 'Acetyl (Protein N-term)'
        * 33% of 'Oxidation (M)'
        * 33% of '2 Oxidation (M)'
        * 33% of 'Unmodified'

        Thus, 33% of sequences are unmodified, implying 66% are modified at least once. If a modification, e.g. Oxidation(M), occurs multiple times in a single peptide it's listed as a separate modification (e.g. '2 Oxidation (M)' for double oxidation of a single peptide).

        Created with MultiQC

        Quantification Analysis

        Peptides Quantification Table

        This plot shows the quantification information of peptides in the final result (mainly the mzTab file).
        The quantification information of peptides is obtained from the MSstats input file. The table shows the quantitative level and distribution of peptides in different study variables, run and peptiforms. The distribution show all the intensity values in a bar plot above and below the average intensity for all the fractions, runs and peptiforms. * BestSearchScore: It is equal to 1 - min(Q.Value) for DIA datasets. Then it is equal to 1 - min(best_search_engine_score[1]), which is from best_search_engine_score[1] column in mzTab peptide table for DDA datasets. * Average Intensity: Average intensity of each peptide sequence across all conditions with NA=0 or NA ignored. * Peptide intensity in each condition (Eg. `CT=Mixture;CN=UPS1;QY=0.1fmol`): Summarize intensity of fractions, and then mean intensity in technical replicates/biological replicates separately.
        Showing 50/50 rows and 6/6 columns.
        PeptideIDProtein NamePeptide SequenceBest Search ScoreAverage IntensityCT=Mixture;CN=UPS1;QY=0.1 fmolCT=Mixture;CN=UPS1;QY=0.25 fmol
        1
        TOLA_ECOLI
        AAAEADDIFGELSSGK
        1.0000
        6.6828
        6.6581
        6.7062
        2
        G3P2_ECOLI
        AAAENIIPHTTGAAK
        0.9998
        6.4660
        6.4003
        6.5230
        3
        RLMH_ECOLI
        AAAEQSWSLSALTLPHPLVR
        1.0000
        7.0008
        6.9572
        7.0405
        4
        PDXJ_ECOLI
        AAAEVGAPFIEIHTGCYADAK
        0.9987
        7.3254
        7.3254
        0.0000
        5
        RL10_ECOLI
        AAAFEGELIPASQIDR
        1.0000
        8.8590
        8.8179
        8.8965
        6
        YFGM_ECOLI
        AAAQLQQGLADTSDENLK
        1.0000
        7.5235
        7.5322
        7.5147
        7
        YFGM_ECOLI
        AAAQLQQGLADTSDENLKAVINLR
        1.0000
        7.3320
        7.1963
        7.4352
        8
        RHLE_ECOLI
        AAATGEALSLVCVDEHK
        0.9998
        6.6031
        6.5730
        6.6313
        9
        SYP_ECOLI
        AAATQEMTLVDTPNAK
        1.0000
        7.1920
        7.1199
        7.2538
        10
        EUTL_ECOLI
        AACNAFTDAVLEIAR
        1.0000
        6.6047
        6.5527
        6.6511
        11
        ACRB_ECOLI
        AADGQMVPFSAFSSSR
        0.9998
        6.5407
        6.3578
        6.6690
        12
        YIDA_ECOLI
        AADGSTVAQTALSYDDYR
        0.9987
        6.6627
        6.7245
        6.6282
        13
        ADHE_ECOLI
        AADIVLQAAIAAGAPK
        1.0000
        8.3796
        8.3536
        8.4041
        14
        NARG_ECOLI
        AADLVDALGQENNPEWK
        1.0000
        6.7428
        6.6793
        6.7981
        15
        OXYR_ECOLI
        AADSCHVSQPTLSGQIR
        1.0000
        7.0733
        7.0424
        7.1021
        16
        TALA_ECOLI
        AAEELEKEGINCNLTLLFSFAQAR
        1.0000
        7.0421
        6.9818
        7.0951
        17
        HEMY_ECOLI
        AAELAGNDTIPVEITR
        1.0000
        7.1336
        7.1077
        7.1581
        18
        SYL_ECOLI
        AAENNPELAAFIDECR
        1.0000
        7.5469
        7.5049
        7.5852
        19
        TALB_ECOLI
        AAEQLEKEGINCNLTLLFSFAQAR
        1.0000
        7.7539
        7.6773
        7.8190
        20
        MBHM_ECOLI
        AAESALNIDVPVNAQYIR
        1.0000
        6.7830
        6.7696
        6.7960
        21
        DNAG_ECOLI
        AAESGVSRPVPQLKR
        0.9993
        5.5067
        5.3646
        5.6135
        22
        HDFR_ECOLI
        AAESLYLTQSAVSFR
        1.0000
        6.4793
        6.4830
        6.4756
        23
        RNE_ECOLI
        AAESRPAPFLIHQESNVIVR
        1.0000
        7.4295
        7.4118
        7.4465
        24
        HFLK_ECOLI
        AAFDDAIAARENEQQYIR
        1.0000
        6.5652
        6.4285
        6.6691
        25
        AROF_ECOLI
        AAFPLSLQQEAQIADSR
        1.0000
        6.4807
        6.3720
        6.5676
        26
        AROF_ECOLI
        AAFPLSLQQEAQIADSRK
        0.9993
        7.0514
        7.0599
        7.0428
        27
        BOLA_ECOLI
        AAFQPVFLEVVDESYR
        1.0000
        7.1941
        7.1723
        7.2149
        28
        CLPB_ECOLI
        AAGATTANITQAIEQMR
        1.0000
        7.5987
        7.6781
        7.5527
        29
        YBIS_ECOLI
        AAGEPLPAVVPAGPDNPMGLYALYIGR
        1.0000
        6.6548
        6.5405
        6.7453
        30
        SDHA_ECOLI
        AAGLHLQESIAEQGALR
        0.9998
        6.0757
        6.0128
        6.1307
        31
        TALA_ECOLI
        AAGLSQYEHLIDDAIAWGK
        0.9992
        6.8263
        6.7789
        6.8482
        32
        TALA_ECOLI
        AAGLSQYEHLIDDAIAWGKK
        1.0000
        6.9728
        6.9756
        6.9699
        33
        ADHE_ECOLI
        AAGVETEVFFEVEADPTLSIVR
        1.0000
        6.9087
        6.9472
        6.8666
        34
        ADHE_ECOLI
        AAGVETEVFFEVEADPTLSIVRK
        0.9998
        7.4544
        7.3854
        7.5139
        35
        ENO_ECOLI
        AAGYELGKDITLAMDCAASEFYK
        1.0000
        7.8751
        7.8493
        7.8994
        36
        YEBE_ECOLI
        AAHQDEPQFGAQSTPLDER
        0.9998
        5.8692
        5.9181
        5.8140
        37
        RIBB_ECOLI
        AAIADGAKPSDLNRPGHVFPLR
        1.0000
        7.2375
        7.1964
        7.2750
        38
        DEOC_ECOLI
        AAIAYGADEVDVVFPYR
        1.0000
        7.5553
        7.4613
        7.6326
        39
        IDH_ECOLI
        AAIEYAIANDRDSVTLVHK
        1.0000
        7.8134
        7.7834
        7.8416
        40
        SYFA_ECOLI
        AAISQASDVAALDNVR
        1.0000
        7.2678
        7.2633
        7.2721
        41
        SYFA_ECOLI
        AAISQASDVAALDNVRVEYLGK
        1.0000
        7.6984
        7.6417
        7.7486
        42
        MUKF_ECOLI
        AAISSCELLLSETSGTLR
        0.9993
        6.1472
        6.1559
        6.1383
        43
        MSCM_ECOLI
        AAKPAQPEVVEALQSALNALEER
        0.9998
        6.0540
        5.9369
        6.1461
        44
        AMPN_ECOLI
        AALEQLKGLENLSGDLYEK
        1.0000
        7.5103
        7.4162
        7.5875
        45
        DBHA_ECOLI
        AALESTLAAITESLK
        1.0000
        7.9537
        7.9562
        7.9512
        46
        HEM3_ECOLI
        AALPPEISLPAVGQGAVGIECR
        0.9993
        6.4280
        6.3064
        6.5230
        47
        SDHA_ECOLI
        AALQISQSGQTCALLSK
        1.0000
        6.1566
        6.0976
        6.2086
        48
        THIP_ECOLI
        AAMLALLQMVCCLGLVLLSQR
        0.9929
        5.8504
        5.8120
        5.8857
        49
        DHE4_ECOLI
        AANAGGVATSGLEMAQNAAR
        1.0000
        6.8077
        6.7297
        6.8737
        50
        GRCA_ECOLI
        AANDDLLNSFWLLDSEK
        0.9998
        6.5810
        6.5594
        6.6016

        Protein Quantification Table

        This plot shows the quantification information of proteins in the final result (mainly the mzTab file).
        The quantification information of proteins is obtained from the msstats input file. The table shows the quantitative level and distribution of proteins in different study variables and run. * Peptides_Number: The number of peptides for each protein. * Average Intensity: Average intensity of each protein across all conditions with NA=0 or NA ignored. * Protein intensity in each condition (Eg. `CT=Mixture;CN=UPS1;QY=0.1fmol`): Summarize intensity of peptides.
        Showing 50/50 rows and 5/5 columns.
        ProteinIDProtein NameNumber of PeptidesAverage IntensityCT=Mixture;CN=UPS1;QY=0.1 fmolCT=Mixture;CN=UPS1;QY=0.25 fmol
        1
        3PASE_ECOLI
        1
        6.9494
        6.8807
        7.0088
        2
        5DNU_ECOLI
        1
        6.1932
        6.1003
        6.2696
        3
        6PGD_ECOLI
        10
        8.3638
        8.3396
        8.3867
        4
        6PGL_ECOLI
        2
        7.6326
        7.5889
        7.6640
        5
        AAS_ECOLI
        3
        6.8951
        6.8911
        6.9008
        6
        AAT_ECOLI
        2
        7.9658
        7.9296
        7.9991
        7
        ABGT_ECOLI
        1
        7.7790
        7.7790
        0.0000
        8
        ACCA_ECOLI
        11
        8.2755
        8.2307
        8.3152
        9
        ACCC_ECOLI
        12
        8.4620
        8.4276
        8.4927
        10
        ACCD_ECOLI
        4
        7.8815
        7.8808
        7.8823
        11
        ACEA_ECOLI
        10
        7.7598
        7.6947
        7.7891
        12
        ACFD_ECOLI
        8
        7.6336
        7.6026
        7.6641
        13
        ACKA_ECOLI
        11
        9.1093
        9.0727
        9.1428
        14
        ACNA_ECOLI
        3
        6.7833
        6.6970
        6.8553
        15
        ACNB_ECOLI
        12
        8.2805
        8.2312
        8.3191
        16
        ACP_ECOLI
        1
        7.6389
        7.5918
        7.6815
        17
        ACRA_ECOLI
        6
        8.1738
        8.1263
        8.2167
        18
        ACRB_ECOLI
        6
        8.3756
        8.3427
        8.4068
        19
        ACSA_ECOLI
        1
        6.1529
        6.0606
        6.1926
        20
        ACUI_ECOLI
        4
        7.8570
        7.7718
        7.9282
        21
        ACYP_ECOLI
        1
        6.3378
        6.3316
        6.3409
        22
        ADD_ECOLI
        6
        8.0337
        8.0400
        8.0307
        23
        ADEC_ECOLI
        1
        5.4576
        5.4344
        5.4797
        24
        ADHE_ECOLI
        33
        9.5549
        9.5068
        9.5983
        25
        ADHP_ECOLI
        2
        6.5932
        6.6095
        6.5762
        26
        ADIA_ECOLI
        3
        7.1124
        7.0835
        7.1395
        27
        ADPP_ECOLI
        2
        7.0229
        6.9876
        7.0556
        28
        AGP_ECOLI
        3
        6.6330
        6.5797
        6.6804
        29
        AHPC_ECOLI
        10
        9.0158
        9.0115
        9.0242
        30
        AHPF_ECOLI
        10
        8.3830
        8.3442
        8.4196
        31
        AK1H_ECOLI
        7
        7.3497
        7.3095
        7.3904
        32
        AK2H_ECOLI
        4
        7.1237
        7.0812
        7.1625
        33
        AK3_ECOLI
        6
        7.4464
        7.2458
        7.4599
        34
        ALAA_ECOLI
        2
        6.8790
        6.8346
        6.9194
        35
        ALAC_ECOLI
        5
        8.0506
        8.0097
        8.0880
        36
        ALDB_ECOLI
        1
        6.4192
        6.4015
        6.4362
        37
        ALF1_ECOLI
        3
        7.1007
        6.8972
        7.0926
        38
        ALF_ECOLI
        12
        9.4015
        9.3588
        9.4395
        39
        ALKH_ECOLI
        4
        7.7365
        7.6386
        7.8109
        40
        ALLE_ECOLI
        1
        6.2522
        6.1514
        6.3340
        41
        ALR1_ECOLI
        3
        7.3731
        7.3394
        7.4043
        42
        AMIA_ECOLI
        1
        5.9071
        5.8856
        5.9276
        43
        AMIB_ECOLI
        2
        6.4927
        6.4137
        6.5595
        44
        AMIC_ECOLI
        4
        6.9237
        6.8522
        6.9852
        45
        AMN_ECOLI
        6
        7.3552
        7.3334
        7.3729
        46
        AMPA_ECOLI
        7
        7.8893
        7.8640
        7.9138
        47
        AMPC_ECOLI
        2
        7.1425
        7.1013
        7.1801
        48
        AMPH_ECOLI
        2
        6.5330
        6.4786
        6.5813
        49
        AMPN_ECOLI
        7
        8.1028
        8.0590
        8.1426
        50
        AMPP_ECOLI
        2
        7.1264
        7.0775
        7.1703

        Intensity Distribution

        log2(Precursor.Quantity) for each Run.
        [DIA-NN: main report] log2(Precursor.Quantity) for each Run.
        Created with MultiQC

        Standard Deviation of Intensity

        Standard deviation of intensity under different experimental conditions.
        [DIA-NN: report.tsv] First, identify the experimental conditions from the "Run" name. Then, group the data by experimental condition and Modified.Sequence, and calculate the standard deviation of log2(Precursor.Quantity).
        Created with MultiQC

        MS1 Analysis

        Total Ion Chromatograms

        MS1 quality control information extracted from the spectrum files.
        This plot displays Total Ion Chromatograms (TICs) derived from MS1 scans across all analyzed samples. The x-axis represents retention time, and the y-axis shows the total ion intensity at each time point. Each colored trace corresponds to a different sample. The TIC provides a global view of the ion signal throughout the LC-MS/MS run, reflecting when compounds elute from the chromatography column. Key aspects to assess include: * Overall intensity pattern: A consistent baseline and similar peak profiles across samples indicate good reproducibility. * Major peak alignment: Prominent peaks appearing at similar retention times suggest stable chromatographic performance. * Signal-to-noise ratio: High peaks relative to baseline noise reflect better sensitivity. * Chromatographic resolution: Sharp, well-separated peaks indicate effective separation. * Signal drift: A gradual decline in signal intensity across the run may point to source contamination or chromatography issues. Deviations such as shifted retention times, missing peaks, or inconsistent intensities may signal problems in sample preparation, LC conditions, or mass spectrometer performance that require further investigation.
        Created with MultiQC

        MS1 Base Peak Chromatograms

        MS1 base peak chromatograms extracted from the spectrum files.
        The Base Peak Chromatogram (BPC) displays the intensity of the most abundant ion at each retention time point across your LC-MS run. Unlike the Total Ion Chromatogram (TIC) which shows the summed intensity of all ions, the BPC highlights the strongest signals, providing better visualization of compounds with high abundance while reducing baseline noise. This makes it particularly useful for identifying major components in complex samples, monitoring dominant species, and providing clearer peak visualization when signal-to-noise ratio is a concern. Comparing BPC patterns across samples allows you to evaluate consistency in the detection of high-abundance compounds and can reveal significant variations in sample composition or instrument performance.
        Created with MultiQC

        MS1 Peaks

        MS1 Peaks from the spectrum files
        This plot shows the number of peaks detected in MS1 scans over the course of each sample run. The x-axis represents retention time (in minutes), while the y-axis displays the number of distinct ion signals (peaks) identified in each MS1 scan. The MS1 peak count reflects spectral complexity and provides insight into instrument performance during the LC-MS analysis. Key aspects to consider include: * Overall pattern: Peak counts typically increase during the elution of complex mixtures and decrease during column washing or re-equilibration phases. * Peak density: Higher counts suggest more complex spectra, potentially indicating a greater number of compounds present at that time point." * Peak Consistency across samples: Similar profiles among replicates or related samples indicate good analytical reproducibility. * Sudden drops: Abrupt decreases in peak count may point to transient ionization issues, spray instability, or chromatographic disruptions. * Baseline values: The minimum peak count observed reflects the level of background noise or instrument sensitivity in the absence of eluting compounds. Monitoring MS1 peak counts complements total ion chromatogram (TIC) and base peak chromatogram (BPC) data, offering an additional layer of quality control related to signal complexity, instrument stability, and sample composition.
        Created with MultiQC

        General stats for MS1 information

        General stats for MS1 information extracted from the spectrum files.
        This table presents general statistics for MS1 information extracted from mass spectrometry data files." It displays MS runs with their acquisition dates and times. For each file, the table shows two key metrics: TotalCurrent (the sum of all MS1 ion intensities throughout the run) and ScanCurrent (the sum of MS2 ion intensities). These values provide a quick overview of the total ion signals detected during both survey scans (MS1) and fragmentation scans (MS2), allowing for comparison of overall signal intensity across samples. Consistent TotalCurrent and ScanCurrent values across similar samples typically indicate good reproducibility in the mass spectrometry analysis, while significant variations may suggest issues with sample preparation, instrument performance, or ionization efficiency. The blue shading helps visualize the relative intensity differences between samples.
        Showing 4/4 rows and 3/3 columns.
        FileAcquisition Date Timelog10(Total Current)log10(Scan Current)
        RD139_Narrow_UPS1_0_1fmol_inj1
        2018-09-01 20:06:01
        12.2519
        12.0890
        RD139_Narrow_UPS1_0_1fmol_inj2
        2018-09-02 11:02:22
        12.2542
        12.1204
        RD139_Narrow_UPS1_0_25fmol_inj1
        2018-09-01 22:09:01
        12.2904
        12.1417
        RD139_Narrow_UPS1_0_25fmol_inj2
        2018-09-02 13:05:24
        12.2901
        12.1573

        Ms1 Area Distribution

        log2(Ms1.Area) for each Run.
        [DIA-NN: report.tsv] log2(Ms1.Area) for each Run. Ms1.Area: non-normalised MS1 peak area.
        Created with MultiQC

        MS2 and Spectral Stats

        Number of Peaks per MS/MS spectrum

        This chart represents a histogram containing the number of peaks per MS/MS spectrum in a given experiment.
        This chart assumes centroid data. Too few peaks can identify poor fragmentation or a detector fault, as opposed to a large number of peaks representing very noisy spectra. This chart is extensively dependent on the pre-processing steps performed to the spectra (centroiding, deconvolution, peak picking approach, etc).
        Created with MultiQC

        Peak Intensity Distribution

        This is a histogram representing the ion intensity vs. the frequency for all MS2 spectra in a whole given experiment. It is possible to filter the information for all, identified and unidentified spectra. This plot can give a general estimation of the noise level of the spectra.
        Generally, one should expect to have a high number of low intensity noise peaks with a low number of high intensity signal peaks. A disproportionate number of high signal peaks may indicate heavy spectrum pre-filtering or potential experimental problems. In the case of data reuse this plot can be useful in identifying the requirement for pre-processing of the spectra prior to any downstream analysis. The quality of the identifications is not linked to this data as most search engines perform internal spectrum pre-processing before matching the spectra. Thus, the spectra reported are not necessarily pre-processed since the search engine may have applied the pre-processing step internally. This pre-processing is not necessarily reported in the experimental metadata.
        Created with MultiQC

        Distribution of Precursor Charges

        This is a bar chart representing the distribution of the precursor ion charges for a given whole experiment.
        [DIA-NN: main report] distribution of the precursor ion charges for a given whole experiment. Precursor.Charge: the charge of the precursor.
        Created with MultiQC

        Charge-state of Per File

        The distribution of the charge-state of the precursor ion.
        [DIA-NN: main report] The distribution of the charge-state of the precursor ion (Precursor.Charge).
        Created with MultiQC


        RT Quality Control

        IDs over RT

        Distribution of retention time, derived from the main report.
        [DIA-NN: main report] Distribution of retention time (RT) for each run.
        Created with MultiQC

        Normalisation Factor over RT

        Distribution of Normalisation.Factor with retention time, derived from the main report.
        [DIA-NN: main report] Distribution of Normalisation.Factor with retention time (RT) for each run. RT: the retention time (RT) of the PSM in minutes. Normalisation.Factor: normalisation factor applied to the precursor in the specific run, i.e. normalised quantity = normalisation factor X non-normalised quantity
        Created with MultiQC

        FWHM over RT

        Distribution of FWHM with retention time, derived from the main report. FWHM: estimated peak width at half-maximum.
        [DIA-NN: main report] Distribution of FWHM with retention time (RT) for each run. RT: the retention time (RT) of the PSM in minutes. FWHM: estimated peak width at half-maximum; note that the accuracy of such estimates sometimes strongly depends on the DIA cycle time and sample injection amount, i.e. they can only be used to evaluate chromatographic performance in direct comparisons with similar settings, including the scan window; another caveat is that FWHM does not reflect any peak tailing.
        Created with MultiQC

        Peak Width over RT

        Distribution of peak width with retention time, derived from the main report. Peak Width = RT.Stop - RT.Start.
        [DIA-NN: main report] Distribution of peak width with retention time (RT) for each run. RT: the retention time (RT) of the PSM in minutes. RT.Start and RT.Stop: peak boundaries.
        Created with MultiQC

        Absolute RT Error over RT

        Distribution of rt error with retention time, derived from the main report.
        [DIA-NN: main report] Distribution of absolute RT error (|RT - Predicted.RT|) with retention time (RT) for each run. RT: the retention time (RT) of the PSM in minutes. Predicted.RT: predicted RT based on the iRT.
        Created with MultiQC

        LOESS RT ~ iRT

        Distribution of LOESS RT ~ iRT for each run, derived from the main report.
        [DIA-NN: main report] Distribution of LOESS RT ~ iRT for each run. RT: the retention time (RT) of the PSM in minutes. iRT: reference RT as recorded in the spectral library.
        Created with MultiQC

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        GroupSoftwareVersion
        ASSEMBLE_EMPIRICAL_LIBRARYDIA-NN2.1.0
        CONVERT_RESULTSquantms-utils0.0.23
        FINAL_QUANTIFICATIONDIA-NN2.1.0
        GENERATE_CFGquantms-utils0.0.23
        INDIVIDUAL_ANALYSISDIA-NN2.1.0
        MSSTATS_LFQbioconductor-msstats4.14.0
        r-base4.4.2
        MZML_STATISTICSquantms-utils0.0.23
        PRELIMINARY_ANALYSISDIA-NN2.1.0
        SAMPLESHEET_CHECKquantms-utils0.0.23
        SDRF_PARSINGsdrf-pipelines0.0.32
        THERMORAWFILEPARSERThermoRawFileParser1.3.4
        WorkflowNextflow24.10.5
        bigbio/quantmsv1.5.0

        bigbio/quantms Workflow Summary

        Input/output options

        export_decoy_psm
        true
        input
        /home/yueqx/Data_Disk/proteogenomics/quantms/test_dia/PXD026600.sdrf.tsv
        outdir
        /home/yueqx/Data_Disk/proteogenomics/quantms/results_dia

        SDRF validation

        skip_factor_validation
        true
        use_ols_cache_only
        true
        validate_ontologies
        true

        Protein database

        database
        /home/yueqx/Data_Disk/proteogenomics/quantms/test_dia/REF_EColi_K12_UPS1_combined.fasta

        Database search

        allowed_missed_cleavages
        1
        max_fr_mz
        1500
        max_mods
        2
        max_peptide_length
        30
        max_pr_mz
        950
        max_precursor_charge
        3
        min_fr_mz
        500
        min_peptide_length
        15
        min_pr_mz
        350

        Modification localization

        luciphor_debug
        0

        PSM re-scoring (general)

        run_fdr_cutoff
        0.10

        PSM re-scoring (Percolator)

        description_correct_features
        0

        Consensus ID

        consensusid_considered_top_hits
        0
        min_consensus_support
        0

        Isobaric analyzer

        quant_activation_method
        HCD

        Protein Quantification (LFQ)

        feature_with_id_min_score
        0.10

        DIA-NN

        diann_normalize
        false

        Statistical post-processing

        contrasts
        pairwise

        Quality control

        enable_pmultiqc
        true
        pmultiqc_idxml_skip
        true

        Institutional config options

        custom_config_base
        /home/yueqx/Data_Disk/proteogenomics/quantms/test_dia/confs

        Generic options

        publish_dir_mode
        symlink
        trace_report_suffix
        2025-07-22_10-44-58

        Core Nextflow options

        configFiles
        N/A
        container
        [withLabel:diann:docker.io/library/diann-2.1.0]
        containerEngine
        docker
        launchDir
        /home/yueqx/Data_Disk/proteogenomics/quantms
        profile
        docker
        projectDir
        /home/yueqx/Data_Disk/proteogenomics/quantms/bigbio/quantms
        runName
        loquacious_wilson
        userName
        yueqx
        workDir
        /home/yueqx/Data_Disk/proteogenomics/quantms/work

        bigbio/quantms Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/bigbio/quantms

        Methods

        Data was processed using bigbio/quantms v1.5.0 (doi: 10.5281/zenodo.7754148) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v24.10.5 (Di Tommaso et al., 2017) with the following command:

        nextflow run bigbio/quantms -profile docker --custom_config_base /home/yueqx/Data_Disk/proteogenomics/quantms/test_dia/confs -c /home/yueqx/Data_Disk/proteogenomics/quantms/test_dia/confs/run_latest_dia.config

        References

        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        Notes:
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.